As industrial machinery has become more complex, machine condition monitoring has received increased attention and evolved into one of the most effective tools for maximizing the economic life-span of industrial machinery in various fields of application. Advanced machine learning techniques are among the key components for sophisticated monitoring systems and provide a means to automatically learn fault diagnosis models from sensor data (e.g., annotated historical data). One of the particular advantages of machine learning in condition monitoring is that the underlying diagnosis models can be adapted both to different application fields and time-shifting monitoring environments.
Arguably one of the most elementary scenarios in machine condition monitoring is to consider only two orthogonal states, namely, the alert state indicating that the system requires specific attention to prevent possible failure or damage and the non-alert state. More sophisticated systems model the machine to be associated with exactly one state from a finite, and typically small, set of alternatives. Systems such as these support a more fine-grained monitoring process such as a green, orange, and red alert scale, where the system states are assumed to be mutually exclusive. Adding even more flexibility, the machine condition might be characterized by a set of states (e.g., failure, alert, etc.) such that more than one state can be applicable at a particular point in time. Prior models of a multi-alert system considered multiple binary monitoring systems where each binary system indicates whether a particular subsystem is in a critical (e.g., relevant, active, and/or alert) state.
With increasingly complex industrial machinery, the need to detect and/or remedy faults (e.g., alerts, failures, etc.) early has become critical. However, prior methods of modeling these faults cannot support a ranking functionality and/or learn to determine a cut-off between active and non-active fault states (e.g., relevant and non-relevant faults), even when this information is specified in the training data.